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Each time, I had to look up what it meant. Reason enough to write about it to make it stick. Upstream downstream concept tracking software components have dependencies to other components. Applying the Dependency Rule, we can safely say that component A is upstream from component B which is upstream from component C even though the arrows point in the other direction.

This is a rather common development style in open source projects: we create a fork of a project, fix a bug or add a feature in that fork and upstream downstream concept tracking submit a patch to the original project. The Value Rule applies as well: since project B adds a new feature or bugfix, it has added value to the original project A.

So, each time we contribute a patch to an open source upstream downstream concept tracking we can say that we have sent a patch upstream. Service B is the upstream service since upstream downstream concept tracking A depends on it. And service A is the downstream service since it adds to the value of service B.

Follow upstream downstream concept tracking on Twitter for more tips on how to become a better software upstream downstream concept tracking. As a professional software engineer, consultant, architect, and general problem solver, I've been practicing the software craft for more than ten years and I'm still learning something new every day.

I love sharing the things I learned, so you and future me can get a head start. Join more than 2, software engineers who get a free weekly email with inspiration to grow as a software engineer.

Join more than 4, readers. Upstream and Downstream Software Dependencies Most software components have dependencies to other components. Consider this figure: Component C depends on component B which in turn depends on component A.

Consider the projects A and B, where A is an original project and B is a fork of A: This is a rather common development style in open source projects: we create a fork of a project, fix a bug or add a feature in that fork and then submit a patch to the original project.

Unsurprisingly, both the Dependency Rules and the Value Rule also apply to this context. The closer a service is to the user or any other end-consumerthe farther downstream it is. Tom Hombergs. Have a look at the previous newsletters to see what's coming. On This Page. Follow Reflectoring.

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However, as industry 4. There are other critical considerations that also underpin the importance of traceability for organizations. These include:. Organizations that start implementing traceability can begin to take advantage of the radically increased insight and control they have over how their products are manufactured and distributed. They can easily avoid problems with manufacturing and when these occur, putting out the flames will be a lot easier.

As the Harvard Business Review ominously warns , this multi-faceted threat faces all businesses, regardless of industry. By implementing a well-designed traceability system, organizations can prevent disaster and access multiple benefits, including:. FORCAM supports organizations with a high-performance solution that helps implement high-performance, well-integrated traceability systems.

We partner with companies looking to ride the wave of the ongoing digital revolution by providing a turnkey and fully flexible IIoT solution that enables them take advantage of the unique opportunities of the digital age. With a global mindset and entrepreneurial spirit, Sandy Abraham works with and across the organization to execute on integrated marketing programs and to initiate compelling new operational models.

Sandy loves pugs, travel, automotive engineering, the aerospace industry, and smart design. What is traceability? Downstream Tracing In downstream tracing, either individual product copies with a unique serial number or lots with a common lot number are traced along the logistics chain from the manufacturer to the Average Speed Of Boat In Upstream And Downstream Full consumer.

Upstream Tracing Upstream tracing refers to the tracing of goods against the logistical chain, i. Internal Traceability In addition to external traceability along the production and supply chain, the internal traceability of parts and products also play a major role in companies. The meaning of Traceability The traceability of products and parts is of paramount importance, especially for companies in the manufacturing industry, as they are forced to maintain the highest quality standards.

How does it work? Why is traceability important? These include: Product recall : Product recalls can be devastating. Traceability is vital for quick, efficient and less costly product recall. It not only helps organizations manage these situations better, traceability also helps prevent avoidable product recalls.

Quality control : With the high-level control it provides over the manufacturing process, traceability encourages granular quality control. This is invaluable in industries such as automotive, food production and defense. Operational efficiency : Too often, the manufacturing process is characterized by waste, especially when caused by factors that may have been prevented if only discovered early enough.

With full traceability systems implemented, organizations can optimize their production processes to enable efficiency and reduce resource wastage. Customer satisfaction : Ultimately, business is about solving the problems of consumers and positioning oneself as a valuable provider of solutions.

With robust traceability systems, organizations are better positioned to implement the incremental improvements that help them present higher-quality products that comprehensively meet customer needs. When there are process challenges during purification, reviewing the following with the upstream team can help to identify the root cause of the problem:.

The process development teams need to openly interact with one another from the very beginning, verifying that changes made to improve or further control the upstream process will not have a negative effect downstream. The open communication and cross-pollination of ideas will also improve the coordination of project timelines and minimize material waste. Additionally, linking upstream and downstream experimental studies could provide benefits to the analytical, product characterization and formulation teams by providing them with material for their studies earlier.

Can they clear it? Will it cause interference? Variability should be minimized � a robust and consistent upstream process is key to a robust and consistent downstream process. We can ensure that we have a robust end-to-end manufacturing process. If you do not receive this email, please contact us at [email protected].

Register to access our FREE online portfolio, request the magazine in print and manage your preferences. You now have unlimited access to all articles from The Medicine Maker. The corresponding model, as shown in Figure 2, will then be something completely different.

Figure 2. A somewhat trivial case of ambiguity: the term Account might mean something very different according to the context it's used in. This is probably the simplest case of ambiguity we can run into when modeling our applications: same term, with two different context dependent meanings. This issue is often resolved by partitioning the name space by adding some prefix to the class name be it the name itself or part of the package.

But on a conceptual level, we must be aware that we have two different contexts in play, sometimes they're different enough to prevent developers from doing mistakes, but sometimes the difference might be subtle. Unfortunately, working at a class name level might not always be a viable solution: in the domain of Banking, the term Banking Account might already exist, with a different meaning, or the domain expert would insist that Account is the right term.

Resist the temptation to invent a specific trade-off term, or to introduce a translation offset from domain expert terms to code. You're facing two separate contexts here. When ambiguity gets in the way, we need a tool to make the development team aware of the existence of two different contexts within the application.

Ambiguity is the super-villain of our Ubiquitous Language, and we need to get rid of it. The best way for doing that is to expose the domain structure in terms of bounded contexts in a context map. Figure 3 shows a simple context map. In the Domain Driven Design book , the Context Map is designated as the primary tool used to make context boundaries explicit.

The basic idea is to draw context boundaries on the whiteboard, optionally filling them up with relevant domain terms of classes. This is not a precise well-defined UML diagram: it's a working tool that allows us to map a fuzzy situation, so a somewhat fuzzy look is necessary. A more puzzling distinction might arise when the underlying concept is the same, but used in different ways, eventually leading to different models.

The model for our Banking Account might be a BankingAccount class like the one shown in Figure 4 below. In this scenario, a payee might be associated with one or more Banking Accounts, but in this case we won't know anything of the internals of payee banking account, nor can we issue any operation on those accounts.

Does it make sense to model the Payee account with the BankingAccount class we've just defined? Still, it doesn't feel completely right: we are not supposed to issue any operation on the payee Banking Account, or to track anything on that. Even worse: doing so would probably be a conceptual mistake within our application. So what should we do? We just ran into two different contexts within the same application again : this time we could end up modeling the same domain concept in two different ways, because we have Average Speed Of Boat In Upstream And Downstream Quantization two clear and distinct uses, each one calling for a distinct model.

The BankingAccount might still be a class that allows us to perform or track specific operation such as deposit or withdraw while a separate class PayeeAccount could possibly have some data in common with BankingAccount such as the accountNumber , but a simpler model and a definitely different behavior we shouldn't be able to access the payees' balance, for example.

Figure 6 shows that despite having a clear meaning and only one underlying concept for the term, we are using it in different ways in our application. This might sound obvious to some, but it's not.

When working on a class diagram, or a UML modeling tool you might really easily start modeling a Payee that has a bankingAccount attribute and think "I've already a class for that".

The pavlovian attempt to get rid of code duplication might do more harm than good, sometimes. A simple context map applied to the example Upstream Downstream Concept Account used before might look as shown in Figure 7 below. Note that as long as our knowledge of the environment increases, that will reflect on the map. Figure 7. A very simple Context Map: drawing contours around portions of domain model shows the areas where conceptual integrity is preserved.

In this case the two contexts have some logical overlapping area: the concept of banking account is used in different ways in different portions of the application, meaning that we'll have different models in play. However, the two models are probably going to interact closely. Besides preserving conceptual integrity of the model within the context boundaries, the context map helps us focus on what's happening between the different contexts.

In this case, assuming that the same team is working on both contexts, we need everybody in the team to be aware of the two different contexts, eventually sharing a translation map, for the terms and concepts that appear in both models. Let's consider again the PFM application. Many of these apps allow some type of data exchange with the financial institutions on-line services. In some cases, banks are providing real time access to home banking services, in some other cases they simply allow users to download bank statements in a common standard format such as Money or Quicken format.

However, from a context mapping perspective, the interactivity and the direction of the communication one way or bi-directional is not relevant. The one thing that does matter is that, once again, we'll have different models in play.

Figure 8. Interaction with an external application naturally calls for a separate bounded context in our Context Map. Even if the two models have been designed to represent the same data at least to a given extent they're different, they will be subject to different evolutionary forces over time, and they serve distinct purposes.

Thus, separate bounded contexts are needed. Example 1 might also fall into this category, if user profiling is modeled using available third party libraries. When our application spans multiple contexts, we need to manage also what happens in-between.

The relationship between different bounded contexts often could provide very important insight about our project. One of the most important things to know is the direction of the relationship between two contexts. DDD uses the terms upstream or downstream: an upstream context will influence the downstream counterpart while the opposite might not be true. This might apply to code libraries depending on one another but also on less technical factors such as schedule or responsiveness to external requests.

In our example we have an external system that clearly won't change according to our requests, while our PFM Banking app will have to be updated quickly, in case the on-line banking services change their APIs for any reason.

Figure 9 illustrates this relationship between the two domain contexts. We can accept to update the way we communicate with the external system, on external demand, but we probably need some kind of protection against changes coming from the upstream context, and to preserve conceptual integrity of our Banking context.

The most suitable pattern here is called Anti-Corruption Layer ACL , and calls for an explicit translation to happen at the code level between the two contexts, or better: on the external boundary of the banking context. This might not only be a technology translation, such as a Java to XML translation, but also a place where to manage all the subtle differences between the models on both ends.

Figure Anti-Corruption Layer on the boundary of PFM Application, preventing the on-line banking services to leak into our bounded context. Not only external systems naturally call for a separate context. An existing legacy component, often has a model that cannot be easily evolved.

Despite being maintained within our organization, even this model is subject to different forces and has been the result of a use which is different from our current one. If we have to interact with a legacy system, chances are high that this will be in a different bounded context. What about the other relationships on the context map? Can we classify them according to the relational DDD patterns?

Since we assumed that development was going on within a single team, the patterns are slightly less interesting here. However, if Banking and Expense tracking were to be maintained by different teams, they're likely to be in a partnership relation: they're both developed towards a common goal and upstream-downstream doesn't make much sense since they're on the same level.

If Web User Profiling is achieved with an external module, we're probably going to use it "as-is", meaning that we're downstream and Conformist, towards Web User Profiling. So far, we considered a simple scenario with only one development team. This allowed us to ignore the cost of communication, assuming maybe optimistically that every developer in the team is aware of "what's going on with the model".

A more complex scenario might include some of the following influencing factors:. Each one of these factors affects the way communication happens within the development teams and the organization in general, eventually shaping the resulting software. A single team, especially in an Agile co-located environment, has many efficient ways to share information among team members: face-to-face conversations, joint design sessions, pair programming, meetings, information radiators and so on.

Unfortunately, these techniques do not scale up so well when team size or number grows, making it hard to share conceptual integrity of the model across the borders of a single development team. After all, agreeing on a model is a quite sophisticated form of communication, involving a common understanding of the problem and a similar view on the possible solution. In scenarios where communication is not that easy, doing becomes a lot cheaper than agreeing.

A classical outcome of this communication bottleneck is the offspring of different classes in different places of the same code base, doing basically the same thing.

Let's suppose now our PFM application grew larger, and another team Team B has been assigned to work with us we're obviously Team A on a new trading module of the same application. Team B might be in a different room, building, city, company or country and is fully dedicated to the trading area. In the example below, Team A shares some of the code with Team B, even if they tend to work on separate portions of the code base.

Eventually, team B writes some class A in Figure 12 below that implements functionalities needed by team B, that were already available in class A. In a scenario where different teams are accessing the same code base, they might have different views on portions of the model.

Physical distribution on the team will affect the quality of the information shared between the teams. That's code duplication, the root of all evil! In a single, well defined, bounded context, this is definitely true.

But for some reason this does happen in almost every non trivial project. This is normally a sign that there are probably not-so-well-separated contexts in play, on the same area of the project. Sometimes, this would make two separated contexts a more efficient way to structure our domain model, than forcing two different teams to continuously integrate their vision. So, how to we draw this on our map? A Context Map reflects our current level of understanding of the whole system, and will be updated as long as we learn something more, or the environment changes.

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